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COMPRESSION OFBIOMEDICAL SIGNALS

DWT AND RUN-LENGTH


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This is a simulation based project

Signal compression is done bypreserving the peak values of ECGsignal

Wavelet Transform is used forfeature extraction

Zero Run-length code is used for

compression

Abstract


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WHAT ARE BIOMEDICAL SIGNALS?

Biomedical signals are observations of

physiological activities of organisms.

ECG,EMG,EEG etc.

The ECG is the electrical manifestation of thecontractile activity of the heart, and can berecorded fairly easily with surface electrodes on

the limbs or chest.

Introduction


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For transmitting and storage applications.

Save crucial time and unnecessarydifficulties in emergency cases.

Reduce the memory cost also.

Need for Compression


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ECG data compression techniques aretypically classified into three major

categories;

q Direct data compressionq Transform coding

q Parameter extraction methods

EXISTING COMPRESSIONMETHODS


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v Reduce the redundancy in data sequence byexamining a successive number of neighboringsamples.

These techniques generally eliminate samples thatcan be implied by examining preceding andsucceeding samples.

Examples :- Amplitude zone epoch coding (AZTEC),Coordinate reduction time encoding system(CORTES).

DIRECT DATACOMPRESSION


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Discrete Wavelet Transform(DWT)


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Need for Compression

Transmission and storage of

uncompressed video would beextremely costly and impractical.

Frame with 352x288 contains 202,752 bytesof information

Recoding of uncompressed version of thisvideo at 15 frames per second wouldrequire 3 MB. One minute 180 MB storage.One 24-hour day 262 GB

Using compression, 15 frames/second for24 hour 1.4 GB, 187 days of video could be


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Fourier Transform

The frequency spectrum of the signal shows

what frequencies exist in the signal

FT

Frequency domain

Temporal domain

No frequency information is available in time-domain

No time information is available in frequency-

domain signal


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What is wavelet transform?

Provides time-frequency

representation Wavelet transform decomposes a

signal into a set of basis functions(wavelets)

Wavelets are obtained from a singleprototype wavelet (t) called motherwavelet by dilations and shifting:

)(1

)(,a

bt

atba

=


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Discrete Wavelet Transform

Theory of WT

The wavelet transform is computedseparately for different segments of the time-domain signal at different frequencies.

Multi-resolution analysis: analyzes the signal

at different frequencies giving differentresolutions

MRA is designed to give good time resolutionand poor frequency resolution at highfrequencies and good frequency resolution

and poor time resolution at low frequencies


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Filterbank Structure:Decomposition


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2-D DWT for Image


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Advantages of DWT over DCT

No need to divide the input coding intonon-overlapping 2-D blocks, it has highercompression ratios avoid blocking artifacts.

Allows good localization both in time and

spatial frequency domain. Transformation of the whole image

introduces inherent scaling

Better identification of which data is

relevant to human perception higher

compression ratio


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Advantages of DWT over DCT (cont.)

Higher flexibility: Wavelet function can befreely chosen

No need to divide the input coding into non-overlapping 2-D blocks, it has highercompression ratios avoid blocking artifacts.

Transformation of the whole image introducesinherent scaling

Better identification of which data is relevantto human perception higher compression

ratio (64:1 vs. 500:1)


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Disadvantages of DCT

Only spatial correlation of the pixelsinside the single 2-D block isconsidered and the correlation fromthe pixels of the neighboring blocks is

neglected

Impossible to completely decorrelatethe blocks at their boundaries usingDCT

Undesirable blockin artifacts affect


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A set of parameters is extracted from the originalsignal which is used in the reconstruction process.

The idea is to quantize a small set of extracted signalfeatures.

PARAMETER EXTRACTIONMETHODS


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Most widely used compression technique.

A linear transformation is applied to thesignal and then compression via

redundancy reduction is applied in thetransform domain rather than in the timedomain.

Fourier transform FT Wavelet transform

TRANSFORM CODING


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In recent years the wavelet transform (WT)has received great attention.

A wavelet is a waveform of effectively limitedduration that has an average value of zero.

Wavelet Transform


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With the blooming of wavelet based

methods in signal processing, muchwork has been done in ECG compressionusing these techniques.

There is a great number of wavelet

compression techniques available in theliterature.

Contd


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The Discrete Wavelet Transform (DWT)has been used in the core of the compressionmodel and has combined with a losslessencoder which is the Runlength Encoder.

Compression using DWT andRunlength

Proposed System


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Block Diagram


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THRESHOLDING

A comparison between a threshold valueis done before encoding.

At the time of comparison the samplevalues greater than or equal to thresholdvalues are preserved.

The samples with a lower value than thethreshold get at the output a zero value.


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RUN LENGTH ENCODING

Runlength encoding process


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Interested information in asignal can be easilypreserved.

Compression ratio is toohigh

Cost effective

Advantages


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Matlab R2009b

Biomedical signalprocessing toolbox

Simulation Tool


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FPGA implementation canbe done to make a realtime system

This method can beincorporated withtelemetry

Future Work


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THANK YOU

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